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\n",
                "text/plain": "<Figure size 432x288 with 1 Axes>"
              }
            ],
            "_view_module": "@jupyter-widgets/output",
            "_model_module_version": "1.0.0",
            "_view_count": null,
            "_view_module_version": "1.0.0",
            "layout": "IPY_MODEL_1c7c6ab66ce4489b881b388c5b80cf9c",
            "_model_module": "@jupyter-widgets/output"
          }
        },
        "1e8ce5f588ae48db98b2ea4c3b630ea3": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "SliderStyleModel",
          "state": {
            "_view_name": "StyleView",
            "handle_color": null,
            "_model_name": "SliderStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
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\n",
                "text/plain": "<Figure size 432x288 with 1 Axes>"
              }
            ],
            "_view_module": "@jupyter-widgets/output",
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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Divyanshu-ISM/Oil-and-Gas-data-analysis/blob/master/gui_practice.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kX_moeL3uMcf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# from tkinter import *"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5GkCt91OuU3B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# d_root = Tk()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5txl-JzeuepI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from ipywidgets import interact,interactive,fixed\n",
        "import ipywidgets as widgets"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-549nePTAXYi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def func(x):\n",
        "  return x**2"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SnV6qdj3CAmt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 83,
          "referenced_widgets": [
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            "30456c86fafc4c08814da9c6c1e34b11",
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            "22ca36cc5af443048c30c1845a2dbda9",
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            "d636ffe51d184a70b53c828fe645354b"
          ]
        },
        "outputId": "82d291d7-bc4e-45bf-a0be-2298b85b09d3"
      },
      "source": [
        "interact(func,x = 5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "31c0fa9fa10647b582b7a34314450de4",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "interactive(children=(IntSlider(value=5, description='x', max=15, min=-5), Output()), _dom_classes=('widget-in…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function __main__.func>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8zPEgIi0CI22",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# the slider is your input x and the value is your output x\n",
        "# and the default is where your slider starts at before you bother it."
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-N2hm16DCipk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y = lambda t : t"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fuxp5MQ7Co3i",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 82,
          "referenced_widgets": [
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      "source": [
        "interact(y,t=10)"
      ],
      "execution_count": 13,
      "outputs": [
        {
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          "data": {
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              "model_id": "e0d3df7c06ec4ec6b6eedd9ffbfde31b",
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              "interactive(children=(IntSlider(value=10, description='t', max=30, min=-10), Output()), _dom_classes=('widget-…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function __main__.<lambda>>"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QrwgySsoDIqg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Interact with Booleans"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BjnSm4C3DgVT",
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      "source": [
        "interact(y,t=True)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
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              "model_id": "06d3773da2b64f1aa07f9160906d7784",
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              "interactive(children=(Checkbox(value=True, description='t'), Output()), _dom_classes=('widget-interact',))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function __main__.<lambda>>"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 16
        }
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ds2RbXmDjnw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Interact with strings\n",
        "def greet(name):\n",
        "  return 'Hey '+name"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fbZ3-PdJD2Zg",
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        "colab": {
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          "height": 84,
          "referenced_widgets": [
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      },
      "source": [
        "interact(greet,name='default_name')"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1c63ce5d96ba439085388ccea13f870f",
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              "interactive(children=(Text(value='default_name', description='name'), Output()), _dom_classes=('widget-interac…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function __main__.greet>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gs3AHi67EA3a",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Hence, interact knows what kind of structure to use based on the datatype your function is dealing with."
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q-waIh-mEb6K",
        "colab_type": "text"
      },
      "source": [
        "#Part 2: Now, rather than passing it all into a function, we can do all that with a decorator. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "toRc6bL9ESh2",
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      "source": [
        "#Example: \n",
        "#1. Conventional Approach\n",
        "\n",
        "def adder(x,y):\n",
        "  return x+y\n",
        "\n",
        "interact(adder,x=5,y=5)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a3bc9f1b4d594960a63e654138e1ed9e",
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              "interactive(children=(IntSlider(value=5, description='x', max=15, min=-5), IntSlider(value=5, description='y',…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function __main__.adder>"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8RcO9wrPE6lU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98,
          "referenced_widgets": [
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      "source": [
        "#Doing it with decorators\n",
        "@interact(x=5,y=5)\n",
        "\n",
        "def adder(x,y):\n",
        "  return x+y\n"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6d68c49aa6de4853a7e45286b991e74f",
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            "text/plain": [
              "interactive(children=(IntSlider(value=5, description='x', max=15, min=-5), IntSlider(value=5, description='y',…"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fz48vWnEG4Ky",
        "colab_type": "text"
      },
      "source": [
        "###The **var =fixed( value)** function fixes the var value and hence we cannot make any changes/toggle/play with that value anymore"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YdHrySDMGafa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
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      "source": [
        "#Doing it with decorators and fixing one value\n",
        "@interact(x=5,y=fixed(5))\n",
        "\n",
        "def adder(x,y):\n",
        "  return x+y\n",
        "\n",
        "#you can see below that this is just adding 5 to the value of x"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "990eff28ed034e348eebea0896b85399",
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              "interactive(children=(IntSlider(value=5, description='x', max=15, min=-5), Output()), _dom_classes=('widget-in…"
            ]
          },
          "metadata": {
            "tags": []
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o357LnxdJRBz",
        "colab_type": "text"
      },
      "source": [
        "#Changing the slider limits\n",
        "## By default the slider min and max are both defined by the default values that we provide. example x = 10 then max is 3*x\n",
        "## we can change this interval by : widgets.IntSlider() methd "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jD9_OKWNKj5e",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EadZSisqKmPv",
        "colab_type": "code",
        "colab": {
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      },
      "source": [
        "@interact(ff,x=0)\n",
        "def ff(x):\n",
        "  return x"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ed94cef1bb5f44f3bb264e36f9d8df84",
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            "text/plain": [
              "interactive(children=(IntSlider(value=0, description='x', max=1), Output()), _dom_classes=('widget-interact',)…"
            ]
          },
          "metadata": {
            "tags": []
          }
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      ]
    },
    {
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        "id": "BHsewpJKHSwB",
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          "base_uri": "https://localhost:8080/",
          "height": 82,
          "referenced_widgets": [
            "994682804ceb4e869aa61a9dcc8b37a8",
            "0ea0e887bba64883b9510d93dc41810a",
            "5d901448a5724be48a221af1fe462c39",
            "a7060eaf1e9d468991355014ae604416",
            "c1f4cd60920541c0abbcaf639bdfd07d",
            "44e5830505594790a08946c7ade312bc",
            "e0f3ab5858b748008b777090fb2589d5"
          ]
        },
        "outputId": "faacfa6b-441f-411e-bfd5-752fe92975c5"
      },
      "source": [
        "interact(ff,x=widgets.IntSlider(min=-50,max=50,step=5,val=0))\n",
        "\n"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "994682804ceb4e869aa61a9dcc8b37a8",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "interactive(children=(IntSlider(value=0, description='x', max=50, min=-50, step=5), Output()), _dom_classes=('…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function __main__.ff>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U7-SIFN2KWYf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 82,
          "referenced_widgets": [
            "8be8bd62c8b64af4be5e480fe942f989",
            "35e020e4685d4011a3c8ff6c9e9eb959",
            "72b6417f17714c3f83d4e0e181446741",
            "fa72da912a7c43ffbbaab24054a985b6",
            "c24af4ea9e7842c88a09df3a0226ce51",
            "4e89d3e26c8e440eb0acab103c761bae",
            "6e8867e3b70c46089826f4081528b862"
          ]
        },
        "outputId": "3446beec-7ad5-41ad-c20c-cc1f6608fcbd"
      },
      "source": [
        "#Abbreviated/Shorthand form of above methodology\n",
        "\n",
        "interact(ff,x=(-30,30,2)) #just like range function"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8be8bd62c8b64af4be5e480fe942f989",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "interactive(children=(IntSlider(value=0, description='x', max=30, min=-30, step=2), Output()), _dom_classes=('…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function __main__.ff>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "is7IialQLVJr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Note that we can also set this as a floating point slider by just changing one/all of these values to floats"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bZNzcU5vL9n5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Doing this with a decorator"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "INQFQNsnMmFM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "2c6fd2b324ed47099cad1f89080952f9",
            "32e5e20375d540e5aa81e52e8abc64f2",
            "360918b9fbea4357ac90c31cc2b6d7bb",
            "1f1f379967c0433b892a0362d8263c41",
            "94b9d2bfc4804b7a962456051c7d0bc0",
            "b534117b69144143a3887741aeb5aa42",
            "d194e66a9de142d49ba77246b7e25b00"
          ]
        },
        "outputId": "8c351c82-2bff-4cf8-fb4c-95f63ba276ae"
      },
      "source": [
        "@interact(x=(-5.0,5.0,0.5))\n",
        "\n",
        "def cuber(x):\n",
        "  return x**3"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2c6fd2b324ed47099cad1f89080952f9",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "interactive(children=(FloatSlider(value=0.0, description='x', max=5.0, min=-5.0, step=0.5), Output()), _dom_cl…"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ikR_oPAkNqro",
        "colab_type": "text"
      },
      "source": [
        "Part 3: Dropdown Menu"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SKLZkWsrMuHc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67,
          "referenced_widgets": [
            "d37864ca7db44a78860f3229604a5cd2",
            "9683b45780cb41ecac6134c415c7c497",
            "79838698e9474fa5ab872e321913a529",
            "e56128d94dd74eb295c68e909c567ea4",
            "0e678f30b5f24bdd9aca5c084e56625b",
            "4bfef11012874f88a62ff996bdd13cbc",
            "15869aa2b8cf46819c2d863d722685fa"
          ]
        },
        "outputId": "3b263c12-f19a-4dd5-ab7d-42fe20cb01b1"
      },
      "source": [
        "#Hence if you provide a list of strings as default parameter, it \n",
        "#generates a dropdown menu\n",
        "@interact(fav_pizza_store = ['Dominos','Pizza Hut','Uncle Sams'])\n",
        "\n",
        "def pizza(fav_pizza_store):\n",
        "  return 'Your choice is: '+fav_pizza_store"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d37864ca7db44a78860f3229604a5cd2",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "interactive(children=(Dropdown(description='fav_pizza_store', options=('Dominos', 'Pizza Hut', 'Uncle Sams'), …"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YW7xQKD3OFwK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67,
          "referenced_widgets": [
            "f6ed4bd082a1458184813ff9caae57ee",
            "e18decd714d4423884d80be84926087c",
            "2398fa687b1641e184bbd514bec92375",
            "faee592ceefc438c85ff8e7f8dbef62e",
            "72d2482f8dbc4bca8fb41a3734e2ba3d",
            "991cb4ab82844df3aaa17ae63baf8db7",
            "9f0d582f23594c9a878051b4c2d0e1ae"
          ]
        },
        "outputId": "3319b06e-6b36-4e80-c4f9-fd2a2ee2f065"
      },
      "source": [
        "#Let's see how it treats a dictionary:\n",
        "\n",
        "@interact(dets = {'name':'Divyanshu','age':25 , 'college': 'IIT ISM'})\n",
        "\n",
        "def showdet(dets):\n",
        "  return dets\n",
        "\n",
        "#Hence if you enter it as a dictionary, It creates a dropdown of keys, where\n",
        "#each selection helps display the corresponding value"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f6ed4bd082a1458184813ff9caae57ee",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "interactive(children=(Dropdown(description='dets', options={'name': 'Divyanshu', 'age': 25, 'college': 'IIT IS…"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BbqEt2HtQK3x",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "###############"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rc-kvphCQoD1",
        "colab_type": "text"
      },
      "source": [
        "# Part 4: The Interactive function\n",
        "It is basically meant to create, name and prepare a widget for future use (in very simple terms)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g2Y_SjuZQnC8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from IPython.display import display\n",
        "\n",
        "#First Step : Define your function with display func included. \n",
        "def multiplier(x,y):\n",
        "  display(x*y)\n",
        "  return x*y\n",
        "\n",
        "#Next step : name your widget using interactive.\n",
        "w = interactive(multiplier,x=10,y=10)"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wo2pGAIXSV8t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Use It : Now we can call and use our widget whenever we want. \n",
        "# Syntax : display(widget_name)"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7_KtoqogSlrJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "3fcf8841ef4049fda7fe7fae15759849",
            "196c58bafa734ee98dda7f54cebbc5c5",
            "2fe57d1d02e94bd6b5b3cb25f2c8d9c8",
            "480afaa0454f43edbe89f4e98fa19d08",
            "0854cee2e0734971b5147d6ede6c96a9",
            "2d42a1cbaa4e48b98b41bddf46317428",
            "e7d1bfe57875489f82f392d7e3e42c45",
            "36622fa76cb842afa6a0e0445aeff909",
            "9a50635b116148a380c3482dcb35973b",
            "69041244848a4ebc9dd824a909fb1e7c"
          ]
        },
        "outputId": "66c48ec7-e626-45c5-f350-e67779dbb2e3"
      },
      "source": [
        "display(w)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3fcf8841ef4049fda7fe7fae15759849",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "interactive(children=(IntSlider(value=10, description='x', max=30, min=-10), IntSlider(value=10, description='…"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xGTFSHeYSnlA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "3fcf8841ef4049fda7fe7fae15759849",
            "196c58bafa734ee98dda7f54cebbc5c5",
            "2fe57d1d02e94bd6b5b3cb25f2c8d9c8",
            "480afaa0454f43edbe89f4e98fa19d08",
            "0854cee2e0734971b5147d6ede6c96a9",
            "2d42a1cbaa4e48b98b41bddf46317428",
            "e7d1bfe57875489f82f392d7e3e42c45",
            "36622fa76cb842afa6a0e0445aeff909",
            "9a50635b116148a380c3482dcb35973b",
            "69041244848a4ebc9dd824a909fb1e7c"
          ]
        },
        "outputId": "e3ec92c3-8c6d-4022-be25-a7408900b467"
      },
      "source": [
        "#Again\n",
        "display(w)\n",
        "#See that both above and below sliders work in sync."
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3fcf8841ef4049fda7fe7fae15759849",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "interactive(children=(IntSlider(value=5, description='x', max=30, min=-10), IntSlider(value=16, description='y…"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BdWfrrpQSvcK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Hence to close a widget you call .close() method on it. \n",
        "w.close()"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ukJB2OzQTwmf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Before close, i could see both of them widgets in cells 50 and 51 (o),\n",
        "#but then as soon as i called close, it just closed them and vanished them off"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9HqrYX6OUNa0",
        "colab_type": "text"
      },
      "source": [
        "Explicitly displaying/assigning etc. the values of a widget."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SWYt2Dm3T81o",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#First Step : Define your function with display func included. \n",
        "def multiplier(x,y):\n",
        "  display(x*y)\n",
        "  return x*y\n",
        "\n",
        "#Next step : name your widget using interactive.\n",
        "w = interactive(multiplier,x=10,y=10)"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8GV9XeVpUVWF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98,
          "referenced_widgets": [
            "b40e43aab4714fd384295daf089bb126",
            "7801120276854601811339cbd9ce87a5",
            "29be417ce08f48afbcacf580137f28cb",
            "e0ccc3ccf5a140b484384e6c278353dc",
            "7d5d1669787045cbbf4f10301172124d",
            "ebf82f9a179941bb9c77c3d6ea785c0b",
            "346c85f246a344ae8859f316b94c5e88",
            "227b57a07de846349622333e64048138",
            "0120abfb8a014e40a9fe521f5c896884",
            "6857710fd42b40cdb5467d412ed3c079"
          ]
        },
        "outputId": "5d4c6f0a-2b61-4f6c-c8a5-982c4191b04f"
      },
      "source": [
        "display(w)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b40e43aab4714fd384295daf089bb126",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "interactive(children=(IntSlider(value=10, description='x', max=30, min=-10), IntSlider(value=10, description='…"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CyKvFIDRUaBY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "w1 = widgets.IntSlider()"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hhxPxcaLUcix",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "152acd73f03e4dce893e140301f8adff",
            "a3a5686f410745dcb1702b754d88cd74",
            "d30c9e7165ab4f22aaa95c1747f3d2fa"
          ]
        },
        "outputId": "b5c7ad05-04a8-471e-c885-1d3ae8e2cff7"
      },
      "source": [
        "display(w1)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "152acd73f03e4dce893e140301f8adff",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "IntSlider(value=0)"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d2ZQj_G3Uz4a",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a333f6fc-7a5d-436c-d6d6-68c095f3e6d8"
      },
      "source": [
        "w1.value"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "68"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OgBdpZaDU4yz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Explicitly changing/assigning a new value. \n",
        "w1.value = 100 #Automatically slides the above w1 display slider. "
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dfm5te-RU92i",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        },
        "outputId": "2c726187-e0a4-4137-b3a0-293d40d3b63d"
      },
      "source": [
        "#hence what's discussed above has got to do with the current display of the widget.\n",
        "#to see what more can be accessed, or can be changed. \n",
        "w1.keys"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['_dom_classes',\n",
              " '_model_module',\n",
              " '_model_module_version',\n",
              " '_model_name',\n",
              " '_view_count',\n",
              " '_view_module',\n",
              " '_view_module_version',\n",
              " '_view_name',\n",
              " 'continuous_update',\n",
              " 'description',\n",
              " 'description_tooltip',\n",
              " 'disabled',\n",
              " 'layout',\n",
              " 'max',\n",
              " 'min',\n",
              " 'orientation',\n",
              " 'readout',\n",
              " 'readout_format',\n",
              " 'step',\n",
              " 'style',\n",
              " 'value']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VhtkC4-ZVuK1",
        "colab_type": "text"
      },
      "source": [
        "#Other widgets/Linked/Hybrid Widgets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2v3fecLiVdEJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Example if we want to display a float not in a slider but in a text box?\n",
        "w_f = widgets.FloatText()\n",
        "display(w_f)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fp06HVE3WDUo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Pretty nice."
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "akipE2YfWJTH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Now Let's do a cool thing. Let's do a slider and text box for floats and link'em.\n",
        "ww_f = widgets.FloatText()\n",
        "ww_s = widgets.FloatSlider()\n",
        "\n",
        "# display(ww_f,ww_s)"
      ],
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aW2wGA3-WjBw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Notice that currently they are not linked. \n",
        "\n",
        "linked = widgets.jslink((ww_f,'value'),(ww_s,'value'))\n",
        "\n",
        "# display(ww_f,ww_s)"
      ],
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uxao9OFkZUTv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 79,
          "referenced_widgets": [
            "08a555aac964405e849533f02bd0f22b",
            "29823ae4c6fe4662969b0d84f39c8ee4",
            "5be967b5decc4f9faf7fde5a293fb55c",
            "d72fbab2b8a449af8985807583e3c92a",
            "66483b4c806a46639d5356f00c62e57a",
            "60de02dec588494e802a604790ce2427"
          ]
        },
        "outputId": "c8b51fae-b069-4223-ad2a-8ad6b14ac446"
      },
      "source": [
        "display(ww_f,ww_s)"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "08a555aac964405e849533f02bd0f22b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "FloatText(value=0.0)"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d72fbab2b8a449af8985807583e3c92a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "FloatSlider(value=40.7)"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1JDRBGaLaM2l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ReCAiDQVcN6z",
        "colab_type": "text"
      },
      "source": [
        "#List of Possible widgets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UxdGcn-5cQs-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Displaying the list of all possible widgets. \n",
        "import ipywidgets as widgets\n",
        "\n",
        "# for item in widgets.Widget.widget_types.items():\n",
        "#   print(item[0][-1])\n",
        "\n",
        "# https://ipywidgets.readthedocs.io/en/latest/examples/Widget%20List.html#Numeric-widgets\n",
        "#Follow this link rather."
      ],
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8-WU5AMrdCV_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221,
          "referenced_widgets": [
            "ec7bb23999c34a79ba9ed7316c80d740",
            "80fa397b9d734db7b1a99ca288e1775b",
            "54d209283284437e84103f2349dd52b2"
          ]
        },
        "outputId": "bdb95646-72c9-4fad-8526-68cf7ae62ff0"
      },
      "source": [
        "widgets.FloatSlider(\n",
        "    value=7.5,\n",
        "    min=0,\n",
        "    max=10.0,\n",
        "    step=0.1,\n",
        "    description='Test:',\n",
        "    disabled=False,\n",
        "    continuous_update=False,\n",
        "    orientation='vertical',\n",
        "    readout=True,\n",
        "    readout_format='.1f',\n",
        ")"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ec7bb23999c34a79ba9ed7316c80d740",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "FloatSlider(value=7.5, continuous_update=False, description='Test:', max=10.0, orientation='vertical', readout…"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n3uvwhVCeqBH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3AIeFLcOgoBM",
        "colab_type": "text"
      },
      "source": [
        "#Example of what a GUI WIDGET can do"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P006fINUgdSH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "%matplotlib inline\n",
        "\n",
        "from ipywidgets import interact, interactive\n",
        "from IPython.display import clear_output, display, HTML\n",
        "\n",
        "import numpy as np\n",
        "from scipy import integrate\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "from matplotlib.colors import cnames\n",
        "from matplotlib import animation"
      ],
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nQ6-7MGLfuVq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def solve_lorenz(N=10, angle=0.0, max_time=4.0, sigma=10.0, beta=8./3, rho=28.0):\n",
        "\n",
        "    fig = plt.figure();\n",
        "    ax = fig.add_axes([0, 0, 1, 1], projection='3d');\n",
        "    ax.axis('off')\n",
        "\n",
        "    # prepare the axes limits\n",
        "    ax.set_xlim((-25, 25))\n",
        "    ax.set_ylim((-35, 35))\n",
        "    ax.set_zlim((5, 55))\n",
        "    \n",
        "    def lorenz_deriv(x_y_z, t0, sigma=sigma, beta=beta, rho=rho):\n",
        "        \"\"\"Compute the time-derivative of a Lorenz system.\"\"\"\n",
        "        x, y, z = x_y_z\n",
        "        return [sigma * (y - x), x * (rho - z) - y, x * y - beta * z]\n",
        "\n",
        "    # Choose random starting points, uniformly distributed from -15 to 15\n",
        "    np.random.seed(1)\n",
        "    x0 = -15 + 30 * np.random.random((N, 3))\n",
        "\n",
        "    # Solve for the trajectories\n",
        "    t = np.linspace(0, max_time, int(250*max_time))\n",
        "    x_t = np.asarray([integrate.odeint(lorenz_deriv, x0i, t)\n",
        "                      for x0i in x0])\n",
        "    \n",
        "    # choose a different color for each trajectory\n",
        "    colors = plt.cm.jet(np.linspace(0, 1, N));\n",
        "\n",
        "    for i in range(N):\n",
        "        x, y, z = x_t[i,:,:].T\n",
        "        lines = ax.plot(x, y, z, '-', c=colors[i])\n",
        "        _ = plt.setp(lines, linewidth=2);\n",
        "\n",
        "    ax.view_init(30, angle)\n",
        "    _ = plt.show();\n",
        "\n",
        "    return t, x_t"
      ],
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e3Q90iPxjFsl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 511,
          "referenced_widgets": [
            "3ec4ee4eda2348949075216249e3fe58",
            "af6a6a400738491faf10f34d26300c93",
            "572e8629427c4640a7ed35b09620ec7f",
            "388fc8d7feb44e35b58309efeb32bbe9",
            "47e5ce91d4ab4f65abef7c98d8c9c13f",
            "18f0deaef679461fb615ffcb8200aaa7",
            "25f87abceeb24dc1904d7fa459506257",
            "117caba6d5e6442e9f18181e99330380",
            "919b515711ea41e3ada5683a4143b9a3",
            "34656a6c6879486c8d8fa33f0fff1d7f",
            "7d88f0280b4c47a89caf74b559107b53",
            "79fad0ea38b34556b7dd68296f5cf3b2",
            "81b6d70d2fd747b1975a0aa75d6d5eac",
            "340fb6d1486444419e64aeae7ee8f799",
            "934a31d66f5a47a2becb892be550b04d",
            "cdd5cf819c824951bd0bc576c32971ca",
            "5ecaaa9fa73a4d3bb220270cea6824e0",
            "073b98c8955548f3b3f52c532a39f973",
            "4dd9f05679c3454e9d7b2cfe98e6c6dd",
            "e8af4ad78fe24d6e9dbd478d6b99e93a",
            "e63066151aa748569f37c80e44490094",
            "a96d8b17d40348bfa37be1ae6e9d6385"
          ]
        },
        "outputId": "37f9c25b-1e57-4cdb-ac3c-9955730e703d"
      },
      "source": [
        "w4 = interactive(solve_lorenz, angle=(0.,360.), N=(0,50), sigma=(0.0,50.0), rho=(0.0,50.0))\n",
        "display(w4);"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3ec4ee4eda2348949075216249e3fe58",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "interactive(children=(IntSlider(value=10, description='N', max=50), FloatSlider(value=0.0, description='angle'…"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sTNzc2dzjMe4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Trying it out in a simple mathematical graph\n",
        "\n",
        "def powerer(n):\n",
        "  x = np.linspace(0,10)\n",
        "  y = x**n\n",
        "  plt.plot(x,y,linewidth=3)\n",
        "  # plt.show()\n",
        "  return x,y"
      ],
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PKLRxLGfjykq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "w5 = interactive(powerer, n = (0,10))"
      ],
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bLjcX-8NkUYl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308,
          "referenced_widgets": [
            "f01f4a6fec0948a7853507782c036e22",
            "1b8c24327844456d8970ead1478fa420",
            "e1df544539164499a4e9083ffec51681",
            "8b5e34d3f930424b9a56bf1ccc94612c",
            "1e8ce5f588ae48db98b2ea4c3b630ea3",
            "f9a9c40996bd43dfb81416f17c0825b3",
            "1c7c6ab66ce4489b881b388c5b80cf9c"
          ]
        },
        "outputId": "1b253b1a-c3d8-49c6-a5c6-a17cc3743bee"
      },
      "source": [
        "display(w5)"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f01f4a6fec0948a7853507782c036e22",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "interactive(children=(IntSlider(value=5, description='n', max=10), Output()), _dom_classes=('widget-interact',…"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aL2j2X8AkWqv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#SOlving an oil and gas problem with interactive widgets. \n",
        "\n",
        "def flow_profiler(mu):\n",
        "  r = np.linspace(2000,0.1)\n",
        "  p = 3000 - (141.2*200*mu*1.1*np.log(2000/r)/(350*30))\n",
        "  plt.plot(r,p)\n",
        "  plt.xlim(0.1,2000)\n",
        "  plt.ylim(500,3000)\n",
        "  plt.xlabel('Radius')\n",
        "  plt.ylabel('Pressure')\n",
        "  plt.title('Flow Profile variation with viscosity')\n",
        "  return p,r\n"
      ],
      "execution_count": 127,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tUgWZlhoma1N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "w_res = interactive(flow_profiler,mu=[100,1000])"
      ],
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pTqHMCHSmtv4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 327,
          "referenced_widgets": [
            "acfce6af3f4e434d8160c9a4115dfad6",
            "fe3cf472eca9474e8ac6481bab6251f2",
            "357bcf73cddf415c9b0c86384e3401a4",
            "cf6a35f1d5c84f468f6e9da9d9fec397",
            "b3dc40bdd57e4886b212ef6bb8def996",
            "004aceaf694548179978ac89221f408d",
            "2fc35e8c93f84ef5ae01b4715a9d5b85"
          ]
        },
        "outputId": "5b7bb139-23fc-4448-94b7-bf324c27bd02"
      },
      "source": [
        "display(w_res)"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "acfce6af3f4e434d8160c9a4115dfad6",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "interactive(children=(Dropdown(description='mu', options=(100, 1000), value=100), Output()), _dom_classes=('wi…"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VOqxU-fcmwp4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}